Link to journal online
Bodenstein, Marc MD; David, Matthias MD; Markstaller, Klaus MD;
Principles of electrical impedance tomography and its clinical application. [Review]
Critical Care Medicine. 37(2):713-724, February 2009.
Abstract
Objective: This review outlines the basic principle, in addition to validated and upcoming clinical use of electrical impedance tomography (EIT). EIT generates functional tomograms of the thorax for detection of changes in regional lung aeration. These images allow an intraindividual comparison of changes in regional lung function. Specifically, EIT aims to optimize ventilation therapy in patients with acute lung failure.Data Sources: PubMed: National Library of Medicine and the National Institutes of Health.Study Selection: Studies with the key words "electrical impedance tomography" since 1983.Data Extraction: Qualitative and quantitative results of the studies.Data Synthesis: We summarize basic principles of the technique and subsequent analyzing methods, and give an overview of clinical and scientific questions that can be addressed by EIT.Conclusion: Potential applications in the future as well as limitations of EIT technology are described. In summary, EIT is a promising functional tomography technology on the verge of its clinical application.
Showing posts with label Electrical impedance tomography. Show all posts
Showing posts with label Electrical impedance tomography. Show all posts
Sunday, 15 February 2009
Thursday, 15 May 2008
Electrical impedance tomography
Link to journal
Schultz, Marcus J. MD, PhD, FCCP
Electrical impedance tomography - A new toy for boys or the future for mechanically ventilated patients? [Editorial]
Critical Care Medicine. 36(4):1380-1381, April 2008.
Costa, Eduardo L. V.; Chaves, Caroline N.; Gomes, Susimeire; Beraldo, Marcelo A.; Volpe, Marcia S. et al
Real-time detection of pneumothorax using electrical impedance tomography
Critical Care Medicine. 36(4):1230-1238, April 2008.
Abstract
Objectives: Pneumothorax is a frequent complication during mechanical ventilation. Electrical impedance tomography (EIT) is a noninvasive tool that allows real-time imaging of regional ventilation. The purpose of this study was to 1) identify characteristic changes in the EIT signals associated with pneumothoraces; 2) develop and fine-tune an algorithm for their automatic detection; and 3) prospectively evaluate this algorithm for its sensitivity and specificity in detecting pneumothoraces in real time.Design: Prospective controlled laboratory animal investigation.Setting: Experimental Pulmonology Laboratory of the University of Sao Paulo.Subjects: Thirty-nine anesthetized mechanically ventilated supine pigs (31.0 +/- 3.2 kg, mean +/- sd).Interventions: In a first group of 18 animals monitored by EIT, we either injected progressive amounts of air (from 20 to 500 mL) through chest tubes or applied large positive end-expiratory pressure (PEEP) increments to simulate extreme lung overdistension. This first data set was used to calibrate an EIT-based pneumothorax detection algorithm. Subsequently, we evaluated the real-time performance of the detection algorithm in 21 additional animals (with normal or preinjured lungs), submitted to multiple ventilatory interventions or traumatic punctures of the lung.Measurements and Main Results: Primary EIT relative images were acquired online (50 images/sec) and processed according to a few imaging-analysis routines running automatically and in parallel. Pneumothoraces as small as 20 mL could be detected with a sensitivity of 100% and specificity 95% and could be easily distinguished from parenchymal overdistension induced by PEEP or recruiting maneuvers. Their location was correctly identified in all cases, with a total delay of only three respiratory cycles.Conclusions: We created an EIT-based algorithm capable of detecting early signs of pneumothoraces in high-risk situations, which also identifies its location. It requires that the pneumothorax occurs or enlarges at least minimally during the monitoring period. Such detection was operator-free and in quasi real-time, opening opportunities for improving patient safety during mechanical ventilation.
Schultz, Marcus J. MD, PhD, FCCP
Electrical impedance tomography - A new toy for boys or the future for mechanically ventilated patients? [Editorial]
Critical Care Medicine. 36(4):1380-1381, April 2008.
Costa, Eduardo L. V.; Chaves, Caroline N.; Gomes, Susimeire; Beraldo, Marcelo A.; Volpe, Marcia S. et al
Real-time detection of pneumothorax using electrical impedance tomography
Critical Care Medicine. 36(4):1230-1238, April 2008.
Abstract
Objectives: Pneumothorax is a frequent complication during mechanical ventilation. Electrical impedance tomography (EIT) is a noninvasive tool that allows real-time imaging of regional ventilation. The purpose of this study was to 1) identify characteristic changes in the EIT signals associated with pneumothoraces; 2) develop and fine-tune an algorithm for their automatic detection; and 3) prospectively evaluate this algorithm for its sensitivity and specificity in detecting pneumothoraces in real time.Design: Prospective controlled laboratory animal investigation.Setting: Experimental Pulmonology Laboratory of the University of Sao Paulo.Subjects: Thirty-nine anesthetized mechanically ventilated supine pigs (31.0 +/- 3.2 kg, mean +/- sd).Interventions: In a first group of 18 animals monitored by EIT, we either injected progressive amounts of air (from 20 to 500 mL) through chest tubes or applied large positive end-expiratory pressure (PEEP) increments to simulate extreme lung overdistension. This first data set was used to calibrate an EIT-based pneumothorax detection algorithm. Subsequently, we evaluated the real-time performance of the detection algorithm in 21 additional animals (with normal or preinjured lungs), submitted to multiple ventilatory interventions or traumatic punctures of the lung.Measurements and Main Results: Primary EIT relative images were acquired online (50 images/sec) and processed according to a few imaging-analysis routines running automatically and in parallel. Pneumothoraces as small as 20 mL could be detected with a sensitivity of 100% and specificity 95% and could be easily distinguished from parenchymal overdistension induced by PEEP or recruiting maneuvers. Their location was correctly identified in all cases, with a total delay of only three respiratory cycles.Conclusions: We created an EIT-based algorithm capable of detecting early signs of pneumothoraces in high-risk situations, which also identifies its location. It requires that the pneumothorax occurs or enlarges at least minimally during the monitoring period. Such detection was operator-free and in quasi real-time, opening opportunities for improving patient safety during mechanical ventilation.
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